Documentation

In the tutorial, when for example, it is written, "with NB201() you can simply query the benchmark", you may provide some examples or precise documentation. It's not clear what you can fill in for the query(arguments exactly).
It seems like you would be able to do NB201.query('cifar10', Metric.TEST_ACCURACY)
Instead of listing the setup and call, you're guided to use a config file. It's better to give the config parameters in the documentation with the function calls. Then say, you can collect these things in config files.

In the video and tutorial, I find it difficult to see why the introductory example doesn't actually train any models and the one using a NAS predictor supposedly does. It seems as if the former happens simply because the NASBenchSearchSpace is loaded, but that stays the same through the second (XGBoost example), there you do have load_labeled=False in one of the calls. That sounds like it may prevent accuracy lookup, but it's more likely that it is in the configuration file that is loaded. I would make it explicit, either in the code or in the configuration file.

Start with the tutorial and video, then inspect the NB201() and NB201.query entry points, including Metric.TEST_ACCURACY. Trace the introductory and XGBoost examples, paying particular attention to NASBenchSearchSpace, load_labeled=False, and the loaded configuration file. Done means the query arguments, equivalent function-call examples, config parameters, and model-training or accuracy-lookup behavior are explicit.

Written by the indexing model from the issue text.